https://github.com/ocampor/image-quality

Image quality is an open source software library for Image Quality Assessment (IQA).
https://github.com/ocampor/image-quality

artificial-intelligence computer-vision image-analysis image-quality-assessment machine-learning python tensorflow

Last synced: 2 months ago
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Image quality is an open source software library for Image Quality Assessment (IQA).

• Host: GitHub
• URL: https://github.com/ocampor/image-quality
• Owner: ocampor
• License: apache-2.0
• Created: 2019-07-06T16:28:23.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-02-05T19:24:49.000Z (12 months ago)
• Last Synced: 2024-11-09T05:13:15.321Z (2 months ago)
• Topics: artificial-intelligence, computer-vision, image-analysis, image-quality-assessment, machine-learning, python, tensorflow
• Language: Python
• Homepage:
• Size: 12.7 MB
• Stars: 399
• Watchers: 10
• Forks: 86
• Open Issues: 20
• Metadata Files:
  • Readme: README.rst
  • Contributing: CONTRIBUTING.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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• awesome-biological-image-analysis - Image Quality - Open source software library for Image Quality Assessment (IQA). (Image restoration and quality assessment)

README

        
.. -*- mode: rst -*-

|Travis|_ |PyPi|_

.. |Travis| image:: https://travis-ci.com/ocampor/image-quality.svg?branch=master

.. _Travis: https://travis-ci.com/ocampor/image-quality

.. |PyPi| image:: https://img.shields.io/pypi/dm/image-quality?color=blue   :alt: PyPI - Downloads

.. _PyPi: https://pypi.org/project/image-quality/

Image Quality

=============

Description

-----------

Image quality is an open source software library for Automatic Image

Quality Assessment (IQA).

Dependencies

------------

-  Python 3.8

-  (Development) Docker

Installation

------------

The package is public and is hosted in PyPi repository. To install it in

your machine run

::

   pip install image-quality

Example

-------

After installing ``image-quality`` package, you can test that it was

successfully installed running the following commands in a python

terminal.

::

   >>> import imquality.brisque as brisque

   >>> import PIL.Image

   >>> path = 'path/to/image'

   >>> img = PIL.Image.open(path)

   >>> brisque.score(img)

   4.9541572815704455

Development

-----------

In case of adding a new tensorflow dataset or modifying the location of a zip file, it is

necessary to update the url checksums. You can find the instructions in the following

`tensorflow documentation `_.

The steps to create the url checksums are the following:

1. Take the file with the dataset configuration (e.g. live_iqa.py) an place it in the ``tensorflow_datasets``

folder. The folder is commonly placed in ``${HOME}/.local/lib/python3.8/site-packages`` if you

install the python packages using the ``user`` flag.

2. Modify the ``__init__.py`` of the ``tensorflow_datasets`` to import your new dataset.

For example ``from .image.live_iqa import LiveIQA`` at the top of the file.

3. In your terminal run the commands:

::

   touch url_checksums/live_iqa.txt

   python -m tensorflow_datasets.scripts.download_and_prepare  \

      --register_checksums  \

      --datasets=live_iqa

4. The file ``live_iqa.txt`` is going to contain the checksum. Now you can copy and paste it to your

project's ``url_checksums`` folder.

Sponsor

-------

.. image:: https://github.com/antonreshetov/mysigmail/raw/master/jetbrains.svg?sanitize=true

   :target: _

Maintainer

----------

- `Ricardo Ocampo `_